1. Field of the Invention
The present invention relates to a radar emitter recognition system and method using a neural network and a fuzzy logic technology for classifying incoming emitters into their functional roles based on operational and physical attributes detected in electronic signatures and the context within which the transmissions were received.
2. Description of the Related Art
Reconnaissance aircraft often include electronic sensor systems capable of intercepting electromagnetic radiation in frequency spectrums typically occupied by offensive and defensive military weapon systems. The radio wave receivers, signal processors and computers in such systems combine to classify the receptions into strategic information used in identifying, not only the sites where the intercepted receptions were originated, but for subsequent missions, specific targeting information. Systems that can accurately classify new sites on the basis that they exhibit similar radar transmission features as previously sensed sites also improves the military's overall command and control, and specifically aids in threat assessment which often proves vital to a mission's success.
The sensor systems available today are faced with identifying a wide variety of disparate electromagnetic transmissions, often emanating from radar sites. A number of variables such as a site's location, terrain, clustering of sites, transmission frequencies, modulation techniques, continuous vs. pulsed transmissions, differing pulse widths, varying power levels, and transmitting antenna radiation patterns make reliable automatic identification of operational and physical characteristics difficult.
The sensing detection systems include wide band receiving antennas, preprocessors, pulse storage buffers and digital signal processors supplying input data to sundry classification schemes. The preprocessors are designed to filter out and retain salient signal features from a transmission's electronic signature. Once such salient features are distilled, pattern recognition techniques use these features to determine a radar site's operational and functional specification. It is common for homing missiles to include such systems in structuring their mission, and to ultimately select a target emitter's electronic signature.
A neural network is an organized arrangement of processing elements which operate in a parallel fashion to solve particular recognition tasks Reference in this regard can be had to Lippmann, Richard P., "An Introduction to Computing with Neural Nets", IEEE ASSP Magazine, April 1987. Conventional neural networks use processing components which typically deal with discrete data that represent the elements from the data base under observation, more particularly in this invention, the features of an emitter's electronic signature. A neural network also contains several functions referred to as layers, such as an input layer, an output layer and one or more hidden layers, each having at least one input node. The nodes and layers cooperate to produce output signals that represent the product of the input signals multiplied by a weight and summing the respective products from each output node. Individual weights are determined during a learning phase which essentially synthesizes the learning patterns to be subsequently recognized. In the most general way the neural network consolidates a multitude of data into a single datum by taking the inner product of a weighted vector and an input vector and comparing the two against a preestablished criterion.
Fuzzy logic systems relate to processes capable of extending conventional Boolean logic principles to handle the more general case of partial truth values, for example as supplied by sensor signals, using algorithms based on rules of inference rather than deterministic mathematical models, which for some problem solving presents a formidable or impossible task. These rules typically follow the form of the familiar mathematical notions of intersection, union and complement. A fuzzy-based classifier compares the variable under observation to a preestablished possibility function, based on known characteristics of the context class, and determines tile variable's similarity to the known context class.
It is known to use neural networks in applications involving signal classification. For example, U.S. Pat. No. 4,945,494 (Penz et al.) discloses an application using neural networks to cluster a plurality of radar signals into classes and a second neural net to identify these classes as known emitter types. Penz et al. does not disclose the utilization of a fuzzy logic paradigm to improve on the accuracy of the classification, and further utilizes two rather than one neural network to cluster the attributes received from an avionic sensor system.
The following U.S. Patents also disclose the use of neural networks in pattern recognition schemes to classify stock market signals, seismic data, radar, sonar, constituents of exhaled breath and two-dimensional images. See U.S. Pat. No. 5,402,520 Schnitta and U.S. Pat. No. 5,248,873 Allen et al. Neither of these patents disclose the use of neural networks combined with fuzzy logic as a construct of the classification solution.
A number of U.S. Patents do disclose recognition systems which classify input signals using both neural networks and fuzzy logic systems: U.S. Pat. No. 5,361,628 Marko et al., U.S. Pat. No. 5,268,835 Miyagaki et al., U.S. Pat. No. 5,204,718 Morita, U.S. Pat. No. 5,179,624 Amano et al., U.S. Pat. No. 5,130,936 Shepard et al., U.S. Pat. No. 5,037,867 Murphy et al., U.S. Pat. No. 5,046,019 Basehore, U.S. Reg. No. H1415 Merryman and U.S. Pat. No. 5,309,921 Kirsner et al. Each of the cited patents differs from the present invention in one or more of the following respects: the manner in which the neural network and fuzzy logic cooperate, the type of neural network employed and the fuzzy logic algorithms used to ascertain the respective degrees of membership in the class under observation. Furthermore, none of these patents employ a probability distance metric which, in accordance with an aspect of the present invention, dramatically improves the classification of emitter-based incoming features.
Presently known systems and methods simply do not combine measurements having both quantitative attribute and qualitative contextual characteristics to determine a functional recognition, as is disclosed by the teachings of this invention.